Differential privacy and Sublinear time are incompatible sometimes

• URL: http://arxiv.org/abs/2407.07262v1
• Date: Tue, 9 Jul 2024 22:33:57 GMT
• Title: Differential privacy and Sublinear time are incompatible sometimes
• Authors: Jeremiah Blocki, Hendrik Fichtenberger, Elena Grigorescu, Tamalika Mukherjee,
• Abstract summary: We show that a simple problem based on one-way marginals yields both a differentially-private algorithm and a sublinear-time algorithm. We do not admit a strictly'' sublinear-time algorithm that is also differentially private.
• Score: 12.776401866635844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differential privacy and sublinear algorithms are both rapidly emerging algorithmic themes in times of big data analysis. Although recent works have shown the existence of differentially private sublinear algorithms for many problems including graph parameter estimation and clustering, little is known regarding hardness results on these algorithms. In this paper, we initiate the study of lower bounds for problems that aim for both differentially-private and sublinear-time algorithms. Our main result is the incompatibility of both the desiderata in the general case. In particular, we prove that a simple problem based on one-way marginals yields both a differentially-private algorithm, as well as a sublinear-time algorithm, but does not admit a ``strictly'' sublinear-time algorithm that is also differentially private.

Related papers

• An Information-Theoretic Approach to Generalization Theory [27.87324770020133]
  We analyze information-theoretic bounds that quantify the dependence between a learning algorithm and the training data. We show that algorithms with a bounded maximal leakage guarantee generalization even with a constant privacy parameter.
  arXiv  Detail & Related papers  (2024-08-20T10:08:21Z)
• Making Old Things New: A Unified Algorithm for Differentially Private Clustering [6.320600791108065]
  We show that a 20-year-old algorithm can be slightly modified to work for any of these models. This provides a unified picture: while matching almost all previously known results, it allows us to improve some of them.
  arXiv  Detail & Related papers  (2024-06-17T15:31:53Z)
• Stochastic Differentially Private and Fair Learning [7.971065005161566]
  We provide the first differentially private algorithm for fair learning that is guaranteed to converge. Our framework is flexible enough to permit different fairness, including demographic parity and equalized odds. Our algorithm can be applied to non-binary classification tasks with multiple (non-binary) sensitive attributes.
  arXiv  Detail & Related papers  (2022-10-17T06:54:57Z)
• Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
  In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
  arXiv  Detail & Related papers  (2021-09-13T18:10:52Z)
• Differentially Private Clustering via Maximum Coverage [7.059472280274009]
  We study the problem of clustering in metric spaces while preserving the privacy of individual data. We present differential algorithms with constant multiplicative error and lower additive error.
  arXiv  Detail & Related papers  (2020-08-27T22:11:18Z)
• Differentially Private Clustering: Tight Approximation Ratios [57.89473217052714]
  We give efficient differentially private algorithms for basic clustering problems. Our results imply an improved algorithm for the Sample and Aggregate privacy framework. One of the tools used in our 1-Cluster algorithm can be employed to get a faster quantum algorithm for ClosestPair in a moderate number of dimensions.
  arXiv  Detail & Related papers  (2020-08-18T16:22:06Z)
• Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms [95.73230376153872]
  This paper studies the relationship between generalization and privacy preservation in iterative learning algorithms by two sequential steps. We prove that $(varepsilon, delta)$-differential privacy implies an on-average generalization bound for multi-Database learning algorithms. We then investigate how the iterative nature shared by most learning algorithms influence privacy preservation and further generalization.
  arXiv  Detail & Related papers  (2020-07-18T09:12:03Z)
• Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis [75.64261155172856]
  survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
  arXiv  Detail & Related papers  (2020-07-06T15:20:17Z)
• The limits of min-max optimization algorithms: convergence to spurious non-critical sets [82.74514886461257]
  min-max optimization algorithms encounter problems far greater because of the existence of periodic cycles and similar phenomena. We show that there exist algorithms that do not attract any points of the problem. We illustrate such challenges in simple to almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost
  arXiv  Detail & Related papers  (2020-06-16T10:49:27Z)
• Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising [51.97494906131859]
  Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc. Existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource. We propose textttNeuSearcher which leverages the knowledge learned from previously instances to solve new problem instances.
  arXiv  Detail & Related papers  (2020-05-09T02:48:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.